Title: Power system state monitoring big data query based on multilevel index

Authors: Zeyuan Zhou; Junrong Liu; Linyan Zhou

Addresses: Information Center of Guizhou Power Grid, Guiyang, 550002, China ' Information Center of Guizhou Power Grid, Guiyang, 550002, China ' Information Center of Guizhou Power Grid, Guiyang, 550002, China

Abstract: The continuous operation of the power system generates a large amount of state data. By querying this data, the operation status of the power system can be judged, which is beneficial for improving the stability of the power system operation. Therefore, a multilevel index based big data query method for power system state monitoring is proposed. Firstly, density clustering algorithm is used to cluster the big data of power system status monitoring. Secondly, based on the clustering results, a distance sensitive hash algorithm is used to represent the mapping relationship of data points, and a multilevel index structure is constructed to complete the query of big data for power system status monitoring. The experimental results show that the proposed method reduces the response time of big data queries for power system status monitoring, improves query throughput and accuracy, and achieves a maximum query accuracy of 94.24%.

Keywords: multilevel index; power system; status monitoring; big data query.

DOI: 10.1504/IJETP.2023.134168

International Journal of Energy Technology and Policy, 2023 Vol.18 No.3/4/5, pp.286 - 297

Received: 27 Apr 2023
Accepted: 03 Jul 2023
Published online: 12 Oct 2023 *

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